contrast_of_interest = 'P_simple_stimlin_high_gt_low'
contrast_of_interest = 'P_simple_stimlin_high_gt_low'
mount_dir = '/Volumes/spacetop_projects_cue/analysis/fmri/spm/univariate/model01_6cond_ttl1/1stlevel';
contrast_name = {'P_VC_cue_high_gt_low', 'V_PC_cue_high_gt_low', 'C_PV_cue_high_gt_low', ...
'P_VC_stimlin_high_gt_low', 'V_PC_stimlin_high_gt_low', 'C_PV_stimlin_high_gt_low',...
'P_VC_stimquad_med_gt_other', 'V_PC_stimquad_med_gt_other', 'C_PV_stimquad_med_gt_other',...
'P_VC_cue_int_stimlin','V_PC_cue_int_stimlin', 'C_PV_cue_int_stimlin',...
'P_VC_cue_int_stimquad','V_PC_cue_int_stimquad','C_PV_cue_int_stimquad',...
'P_simple_cue_high_gt_low', 'V_simple_cue_high_gt_low', 'C_simple_cue_high_gt_low', ...
'P_simple_stimlin_high_gt_low', 'V_simple_stimlin_high_gt_low', 'C_simple_stimlin_high_gt_low',...
'P_simple_stimquad_m`ed_gt_other', 'V_simple_stimquad_med_gt_other', 'C_simple_stimquad_med_gt_other',...
'P_simple_cue_int_stimlin', 'V_simple_cue_int_stimlin', 'C_simple_cue_int_stimlin',...
'P_simple_cue_int_stimquad','V_simple_cue_int_stimquad','C_simple_cue_int_stimquad'
index = find(strcmp(contrast_name, contrast_of_interest));
con_name = sprintf('*con_%04d.nii', index);
con_list = dir(fullfile(mount_dir, '*', con_name));
con_fldr = {con_list.folder}; fname = {con_list.name};
con_files = strcat(con_fldr,'/', fname)';
con_data_obj = fmri_data(con_files);
Using default mask: /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/canlab_canonical_brains/Canonical_brains_surfaces/brainmask_canlab.nii
Direct calls to spm_defauts are deprecated.
Please use spm('Defaults',modality) or spm_get_defaults instead.
loading mask. mapping volumes.
checking that dimensions and voxel sizes of volumes are the same.
Pre-allocating data array. Needed: 32746536 bytes
Loading image number: 82
Elapsed time is 3.459096 seconds.
Image names entered, but fullpath attribute is empty. Getting path info.
Number of unique values in dataset: 7780429 Bit rate: 22.89 bits
disp(strcat("current length is ", num2str(size(con_data_obj.dat,2))));
%for s = 1:length(wh_outlier_corr)
%disp(strcat("-------subject", num2str(s), "------"))
con.dat = con_data_obj.dat(:,~wh_outlier_corr);
con.image_names = con_data_obj.image_names(~wh_outlier_corr,:);
con.fullpath = con_data_obj.fullpath(~wh_outlier_corr,:);
con.files_exist = con_data_obj.files_exist(~wh_outlier_corr,:);
disp(strcat("after removing ", num2str(sum(wh_outlier_corr)), " participants, size is now ",num2str(size(con.dat,2))));
after removing 3 participants, size is now 79
[path,n,e] = fileparts(con_fldr(wh_outlier_corr));
disp(strcat("participants that are outliers:... ", n));
"participants that are outliers:... sub-0093" "participants that are outliers:... sub-0106" "participants that are outliers:... sub-0118"
disp(n);
{'sub-0093'} {'sub-0106'} {'sub-0118'}
t = ttest(imgs2);
One-sample t-test
Calculating t-statistics and p-values
orthviews(t);
SPM12: spm_check_registration (v7759) 19:54:50 - 22/10/2023
========================================================================
Display /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/canlab_canonical_brains/Canonical_brains_surfaces/keuken_2014_enhanced_for_underlay.img,1
fdr_t = threshold(t, .05, 'fdr');
Image 1 FDR q < 0.050 threshold is 0.038126
Image 1
18 contig. clusters, sizes 1 to 76015
Positive effect: 74395 voxels, min p-value: 0.00000000
Negative effect: 1741 voxels, min p-value: 0.00000000
orthviews(fdr_t);
SPM12: spm_check_registration (v7759) 19:54:51 - 22/10/2023
========================================================================
Display /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/canlab_canonical_brains/Canonical_brains_surfaces/keuken_2014_enhanced_for_underlay.img,1
fdr_t = threshold(t, .001, 'fdr');
Image 1 FDR q < 0.001 threshold is 0.000509
Image 1
37 contig. clusters, sizes 1 to 50469
Positive effect: 50645 voxels, min p-value: 0.00000000
Negative effect: 185 voxels, min p-value: 0.00000000
orthviews(fdr_t);
SPM12: spm_check_registration (v7759) 19:54:52 - 22/10/2023
========================================================================
Display /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/canlab_canonical_brains/Canonical_brains_surfaces/keuken_2014_enhanced_for_underlay.img,1
create_figure('montage'); axis off;
montage(fdr_t);
Setting up fmridisplay objects
sagittal montage: 1330 voxels displayed, 49500 not displayed on these slices
sagittal montage: 1360 voxels displayed, 49470 not displayed on these slices
sagittal montage: 1220 voxels displayed, 49610 not displayed on these slices
axial montage: 8707 voxels displayed, 42123 not displayed on these slices
axial montage: 9428 voxels displayed, 41402 not displayed on these slices

write(fdr_t, 'fname', strcat('/Users/h/Desktop/', contrast_of_interest, '_fdr_t.nii'), 'overwrite');
Writing:
/Users/h/Desktop/P_simple_stimlin_high_gt_low_fdr_t.nii
[image_by_feature_correlations, top_feature_tables] = neurosynth_feature_labels( mean(imgs2), 'images_are_replicates', false, 'noverbose');
Input image 1
fullpath_was_empty
_____________________________________________________________________
testr_low words_low testr_high words_high
_________ ______________ __________ _________________
-0.14035 {'trait' } 0.25428 {'motor' }
-0.13912 {'depression'} 0.2493 {'movements' }
-0.13912 {'disorder' } 0.225 {'sensorimotor' }
-0.13899 {'judgment' } 0.20722 {'finger' }
-0.13814 {'affect' } 0.20564 {'hand' }
-0.13107 {'face' } 0.18631 {'somatosensory'}
-0.12674 {'faces' } 0.18519 {'muscle' }
-0.12648 {'affective' } 0.1831 {'execution' }
-0.12602 {'work' } 0.18167 {'sensory' }
-0.12529 {'social' } 0.181 {'production' }
% [image_by_feature_correlations, top_feature_tables] = neurosynth_feature_labels( m, 'images_are_replicates', false, 'noverbose');
[obj, names] = load_image_set('pain_cog_emo');
Loaded images:
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_pMCC_Pain.nii
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_aMCC_Pain.nii
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_pACC_Pain.nii
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_sgACC_Pain.nii
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_vmPFC_Pain.nii
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_dMFC_Pain.nii
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_MFC_Pain.nii
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_Wholebrain_Pain.nii
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_pMCC_Cognitive_Control.nii
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_aMCC_Cognitive_Control.nii
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_pACC_Cognitive_Control.nii
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_sgACC_Cognitive_Control.nii
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_vmPFC_Cognitive_Control.nii
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_dMFC_Cognitive_Control.nii
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_MFC_Cognitive_Control.nii
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_Wholebrain_Cognitive_Control.nii
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_pMCC_Negative_Emotion.nii
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_aMCC_Negative_Emotion.nii
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_pACC_Negative_Emotion.nii
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_sgACC_Negative_Emotion.nii
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_vmPFC_Negative_Emotion.nii
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_dMFC_Negative_Emotion.nii
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_MFC_Negative_Emotion.nii
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_Wholebrain_Negative_Emotion.nii
bpls_wholebrain = get_wh_image(obj, [8 16 24]);
names_wholebrain = names([8 16 24]);
create_figure('Kragel Pain-Cog-Emo maps', 1, 3);
stats = image_similarity_plot(con_data_obj, 'average', 'mapset', bpls_wholebrain, 'networknames', names_wholebrain, 'nofigure');
Table of correlations Group:1
--------------------------------------
T-test on Fisher's r to Z transformed point-biserial correlations
R_avg T P sig
Pain Wholebrain 0.0041 0.6995 0.4862 0.0000
Cog Wholebrain 0.0118 3.1482 0.0023 1.0000
Emo Wholebrain -0.0145 -2.7458 0.0074 1.0000
barplot_columns(stats.r', 'nofigure', 'colors', {[1 .9 0] [.2 .2 1] [1 .2 .2]}, 'names', names_wholebrain)
Col 1: Pain Wholebrain Col 2: Cog Wholebrain Col 3: Emo Wholebrain
---------------------------------------------
Tests of column means against zero
---------------------------------------------
Name Mean_Value Std_Error T P Cohens_d
___________________ __________ _________ _______ _________ ________
{'Pain Wholebrain'} 0.0041091 0.0059246 0.69357 0.48994 0.076592
{'Cog Wholebrain' } 0.011818 0.003754 3.1481 0.0023011 0.34765
{'Emo Wholebrain' } -0.014474 0.0052736 -2.7446 0.0074599 -0.30309
ans =
fig_han: [1×1 struct]
axis_han: [1×1 Axes]
bar_han1: [1×1 Bar]
bar_han: {[1×1 Bar] [1×1 Bar] [1×1 Bar]}
errorbar_han: {[1×1 ErrorBar] [1×1 ErrorBar] [1×1 ErrorBar]}
point_han1: {82×3 cell}
text_han: {82×3 cell}
point_han: {82×3 cell}
star_handles: [16.0006 17.0006 18.0006]
ylabel('Pattern similarity (r)');
title('Similarity (r) with patterns')
test_data_obj = resample_space(con_data_obj, bpls_wholebrain);
csim(:, i) = canlab_pattern_similarity(test_data_obj.dat, bpls_wholebrain.dat(:, i), 'cosine_similarity');
end
Warning: Some images have zero values in some of the 411578 voxels in weight mask. These will be excluded from similarity analysis image-wise.
Number of zero or NaN values within weight mask, by input image:
83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83
Warning: Some images have zero values in some of the 411578 voxels in weight mask. These will be excluded from similarity analysis image-wise.
Number of zero or NaN values within weight mask, by input image:
83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83
Warning: Some images have zero values in some of the 411578 voxels in weight mask. These will be excluded from similarity analysis image-wise.
Number of zero or NaN values within weight mask, by input image:
83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83
barplot_columns(csim, 'nofigure', 'colors', {[1 .9 0] [.2 .2 1] [1 .2 .2]}, 'names', names_wholebrain)
Col 1: Pain Wholebrain Col 2: Cog Wholebrain Col 3: Emo Wholebrain
---------------------------------------------
Tests of column means against zero
---------------------------------------------
Name Mean_Value Std_Error T P Cohens_d
___________________ __________ _________ _______ _________ ________
{'Pain Wholebrain'} 0.0074956 0.0056994 1.3152 0.19217 0.14523
{'Cog Wholebrain' } 0.011004 0.0034697 3.1715 0.0021426 0.35023
{'Emo Wholebrain' } -0.017319 0.0051091 -3.3899 0.0010827 -0.37435
ans =
fig_han: [1×1 struct]
axis_han: [1×1 Axes]
bar_han1: [1×1 Bar]
bar_han: {[1×1 Bar] [1×1 Bar] [1×1 Bar]}
errorbar_han: {[1×1 ErrorBar] [1×1 ErrorBar] [1×1 ErrorBar]}
point_han1: {82×3 cell}
text_han: {82×3 cell}
point_han: {82×3 cell}
star_handles: [19.0006 20.0006 21.0006]
ylabel('Pattern similarity (cosine sim)');
title('Pattern response (cosine similarity)')
contrast_of_interest = 'V_simple_stimlin_high_gt_low'
contrast_of_interest = 'V_simple_stimlin_high_gt_low'
mount_dir = '/Volumes/spacetop_projects_cue/analysis/fmri/spm/univariate/model01_6cond_ttl1/1stlevel';
contrast_name = {'P_VC_cue_high_gt_low', 'V_PC_cue_high_gt_low', 'C_PV_cue_high_gt_low', ...
'P_VC_stimlin_high_gt_low', 'V_PC_stimlin_high_gt_low', 'C_PV_stimlin_high_gt_low',...
'P_VC_stimquad_med_gt_other', 'V_PC_stimquad_med_gt_other', 'C_PV_stimquad_med_gt_other',...
'P_VC_cue_int_stimlin','V_PC_cue_int_stimlin', 'C_PV_cue_int_stimlin',...
'P_VC_cue_int_stimquad','V_PC_cue_int_stimquad','C_PV_cue_int_stimquad',...
'P_simple_cue_high_gt_low', 'V_simple_cue_high_gt_low', 'C_simple_cue_high_gt_low', ...
'P_simple_stimlin_high_gt_low', 'V_simple_stimlin_high_gt_low', 'C_simple_stimlin_high_gt_low',...
'P_simple_stimquad_med_gt_other', 'V_simple_stimquad_med_gt_other', 'C_simple_stimquad_med_gt_other',...
'P_simple_cue_int_stimlin', 'V_simple_cue_int_stimlin', 'C_simple_cue_int_stimlin',...
'P_simple_cue_int_stimquad','V_simple_cue_int_stimquad','C_simple_cue_int_stimquad'
index = find(strcmp(contrast_name, contrast_of_interest));
con_name = sprintf('*con_%04d.nii', index);
con_list = dir(fullfile(mount_dir, '*', con_name));
con_fldr = {con_list.folder}; fname = {con_list.name};
con_files = strcat(con_fldr,'/', fname)';
con_data_obj = fmri_data(con_files);
Using default mask: /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/canlab_canonical_brains/Canonical_brains_surfaces/brainmask_canlab.nii
Direct calls to spm_defauts are deprecated.
Please use spm('Defaults',modality) or spm_get_defaults instead.
loading mask. mapping volumes.
checking that dimensions and voxel sizes of volumes are the same.
Pre-allocating data array. Needed: 32746536 bytes
Loading image number: 82
Elapsed time is 3.308815 seconds.
Image names entered, but fullpath attribute is empty. Getting path info.
Number of unique values in dataset: 7827346 Bit rate: 22.90 bits
disp(strcat("current length is ", num2str(size(con_data_obj.dat,2))));
%for s = 1:length(wh_outlier_corr)
% disp(strcat("-------subject", num2str(s), "------"))
con.dat = con_data_obj.dat(:,~wh_outlier_corr);
con.image_names = con_data_obj.image_names(~wh_outlier_corr,:);
con.fullpath = con_data_obj.fullpath(~wh_outlier_corr,:);
con.files_exist = con_data_obj.files_exist(~wh_outlier_corr,:);
disp(strcat("after removing ", num2str(sum(wh_outlier_corr)), " participants, size is now ",num2str(size(con.dat,2))));
after removing 6 participants, size is now 76
[path,n,e] = fileparts(con_fldr(wh_outlier_corr));
disp(strcat("participants that are outliers:... ", n));
"participants that are …" "participants that are …" "participants that are …" "participants that are …" "participants that are …" "participants that are …"
disp(n);
{'sub-0082'} {'sub-0085'} {'sub-0086'} {'sub-0098'} {'sub-0114'} {'sub-0123'}
[obj, names] = load_image_set('pain_cog_emo');
Loaded images:
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_pMCC_Pain.nii
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_aMCC_Pain.nii
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_pACC_Pain.nii
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_sgACC_Pain.nii
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_vmPFC_Pain.nii
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_dMFC_Pain.nii
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_MFC_Pain.nii
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_Wholebrain_Pain.nii
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_pMCC_Cognitive_Control.nii
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_aMCC_Cognitive_Control.nii
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_pACC_Cognitive_Control.nii
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_sgACC_Cognitive_Control.nii
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_vmPFC_Cognitive_Control.nii
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_dMFC_Cognitive_Control.nii
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_MFC_Cognitive_Control.nii
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_Wholebrain_Cognitive_Control.nii
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_pMCC_Negative_Emotion.nii
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_aMCC_Negative_Emotion.nii
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_pACC_Negative_Emotion.nii
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_sgACC_Negative_Emotion.nii
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_vmPFC_Negative_Emotion.nii
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_dMFC_Negative_Emotion.nii
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_MFC_Negative_Emotion.nii
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_Wholebrain_Negative_Emotion.nii
bpls_wholebrain = get_wh_image(obj, [8 16 24]);
names_wholebrain = names([8 16 24]);
create_figure('Kragel Pain-Cog-Emo maps', 1, 3);
stats = image_similarity_plot(con_data_obj, 'average', 'mapset', bpls_wholebrain, 'networknames', names_wholebrain, 'nofigure');
Table of correlations Group:1
--------------------------------------
T-test on Fisher's r to Z transformed point-biserial correlations
R_avg T P sig
Pain Wholebrain -0.0087 -1.4055 0.1637 0.0000
Cog Wholebrain -0.0029 -0.7534 0.4534 0.0000
Emo Wholebrain 0.0108 1.6418 0.1045 0.0000
barplot_columns(stats.r', 'nofigure', 'colors', {[1 .9 0] [.2 .2 1] [1 .2 .2]}, 'names', names_wholebrain)
Col 1: Pain Wholebrain Col 2: Cog Wholebrain Col 3: Emo Wholebrain
---------------------------------------------
Tests of column means against zero
---------------------------------------------
Name Mean_Value Std_Error T P Cohens_d
___________________ __________ _________ ________ _______ _________
{'Pain Wholebrain'} -0.0086758 0.0061716 -1.4058 0.16362 -0.15524
{'Cog Wholebrain' } -0.0029383 0.003903 -0.75284 0.45373 -0.083137
{'Emo Wholebrain' } 0.010843 0.0066041 1.6419 0.10449 0.18132
ans =
fig_han: [1×1 struct]
axis_han: [1×1 Axes]
bar_han1: [1×1 Bar]
bar_han: {[1×1 Bar] [1×1 Bar] [1×1 Bar]}
errorbar_han: {[1×1 ErrorBar] [1×1 ErrorBar] [1×1 ErrorBar]}
point_han1: {82×3 cell}
text_han: {82×3 cell}
point_han: {82×3 cell}
star_handles: [16.0007 17.0007 18.0007]
ylabel('Pattern similarity (r)');
title('Similarity (r) with patterns')
test_data_obj = resample_space(con_data_obj, bpls_wholebrain);
csim(:, i) = canlab_pattern_similarity(test_data_obj.dat, bpls_wholebrain.dat(:, i), 'cosine_similarity');
end
Warning: Some images have zero values in some of the 411578 voxels in weight mask. These will be excluded from similarity analysis image-wise.
Number of zero or NaN values within weight mask, by input image:
83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83
Warning: Some images have zero values in some of the 411578 voxels in weight mask. These will be excluded from similarity analysis image-wise.
Number of zero or NaN values within weight mask, by input image:
83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83
Warning: Some images have zero values in some of the 411578 voxels in weight mask. These will be excluded from similarity analysis image-wise.
Number of zero or NaN values within weight mask, by input image:
83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83
barplot_columns(csim, 'nofigure', 'colors', {[1 .9 0] [.2 .2 1] [1 .2 .2]}, 'names', names_wholebrain)
Col 1: Pain Wholebrain Col 2: Cog Wholebrain Col 3: Emo Wholebrain
---------------------------------------------
Tests of column means against zero
---------------------------------------------
Name Mean_Value Std_Error T P Cohens_d
___________________ __________ _________ ________ ________ _________
{'Pain Wholebrain'} -0.008612 0.006035 -1.427 0.15742 -0.15759
{'Cog Wholebrain' } -0.0028725 0.0036104 -0.79563 0.42857 -0.087862
{'Emo Wholebrain' } 0.010898 0.00646 1.6869 0.095465 0.18629
ans =
fig_han: [1×1 struct]
axis_han: [1×1 Axes]
bar_han1: [1×1 Bar]
bar_han: {[1×1 Bar] [1×1 Bar] [1×1 Bar]}
errorbar_han: {[1×1 ErrorBar] [1×1 ErrorBar] [1×1 ErrorBar]}
point_han1: {82×3 cell}
text_han: {82×3 cell}
point_han: {82×3 cell}
star_handles: [19.0007 20.0007 21.0007]
ylabel('Pattern similarity (cosine sim)');
title('Pattern response (cosine similarity)')
contrast_of_interest = 'C_simple_stimlin_high_gt_low';
mount_dir = '/Volumes/spacetop_projects_cue/analysis/fmri/spm/univariate/model01_6cond_ttl1/1stlevel';
contrast_name = {'P_VC_cue_high_gt_low', 'V_PC_cue_high_gt_low', 'C_PV_cue_high_gt_low', ...
'P_VC_stimlin_high_gt_low', 'V_PC_stimlin_high_gt_low', 'C_PV_stimlin_high_gt_low',...
'P_VC_stimquad_med_gt_other', 'V_PC_stimquad_med_gt_other', 'C_PV_stimquad_med_gt_other',...
'P_VC_cue_int_stimlin','V_PC_cue_int_stimlin', 'C_PV_cue_int_stimlin',...
'P_VC_cue_int_stimquad','V_PC_cue_int_stimquad','C_PV_cue_int_stimquad',...
'P_simple_cue_high_gt_low', 'V_simple_cue_high_gt_low', 'C_simple_cue_high_gt_low', ...
'P_simple_stimlin_high_gt_low', 'V_simple_stimlin_high_gt_low', 'C_simple_stimlin_high_gt_low',...
'P_simple_stimquad_m`ed_gt_other', 'V_simple_stimquad_med_gt_other', 'C_simple_stimquad_med_gt_other',...
'P_simple_cue_int_stimlin', 'V_simple_cue_int_stimlin', 'C_simple_cue_int_stimlin',...
'P_simple_cue_int_stimquad','V_simple_cue_int_stimquad','C_simple_cue_int_stimquad'
index = find(strcmp(contrast_name, contrast_of_interest));
con_name = sprintf('*con_%04d.nii', index);
con_list = dir(fullfile(mount_dir, '*', con_name));
con_fldr = {con_list.folder}; fname = {con_list.name};
con_files = strcat(con_fldr,'/', fname)';
con_data_obj = fmri_data(con_files);
Using default mask: /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/canlab_canonical_brains/Canonical_brains_surfaces/brainmask_canlab.nii
Direct calls to spm_defauts are deprecated.
Please use spm('Defaults',modality) or spm_get_defaults instead.
loading mask. mapping volumes.
checking that dimensions and voxel sizes of volumes are the same.
Pre-allocating data array. Needed: 32746536 bytes
Loading image number: 82
Elapsed time is 3.279799 seconds.
Image names entered, but fullpath attribute is empty. Getting path info.
Number of unique values in dataset: 7823231 Bit rate: 22.90 bits
disp(strcat("current length is ", num2str(size(con_data_obj.dat,2))));
%for s = 1:length(wh_outlier_corr)
% disp(strcat("-------subject", num2str(s), "------"))
con.dat = con_data_obj.dat(:,~wh_outlier_corr);
con.image_names = con_data_obj.image_names(~wh_outlier_corr,:);
con.fullpath = con_data_obj.fullpath(~wh_outlier_corr,:);
con.files_exist = con_data_obj.files_exist(~wh_outlier_corr,:);
disp(strcat("after removing ", num2str(sum(wh_outlier_corr)), " participants, size is now ",num2str(size(con.dat,2))));
after removing 4 participants, size is now 78
[path,n,e] = fileparts(con_fldr(wh_outlier_corr));
disp(strcat("participants that are outliers:... ", n));
"participants that are outliers:... su…" "participants that are outliers:... su…" "participants that are outliers:... su…" "participants that are outliers:... su…"
disp(n);
{'sub-0082'} {'sub-0085'} {'sub-0120'} {'sub-0123'}
[obj, names] = load_image_set('pain_cog_emo');
Loaded images:
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_pMCC_Pain.nii
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_aMCC_Pain.nii
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_pACC_Pain.nii
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_sgACC_Pain.nii
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_vmPFC_Pain.nii
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_dMFC_Pain.nii
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_MFC_Pain.nii
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_Wholebrain_Pain.nii
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_pMCC_Cognitive_Control.nii
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_aMCC_Cognitive_Control.nii
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_pACC_Cognitive_Control.nii
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_sgACC_Cognitive_Control.nii
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_vmPFC_Cognitive_Control.nii
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_dMFC_Cognitive_Control.nii
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_MFC_Cognitive_Control.nii
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_Wholebrain_Cognitive_Control.nii
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_pMCC_Negative_Emotion.nii
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_aMCC_Negative_Emotion.nii
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_pACC_Negative_Emotion.nii
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_sgACC_Negative_Emotion.nii
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_vmPFC_Negative_Emotion.nii
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_dMFC_Negative_Emotion.nii
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_MFC_Negative_Emotion.nii
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_Wholebrain_Negative_Emotion.nii
bpls_wholebrain = get_wh_image(obj, [8 16 24]);
names_wholebrain = names([8 16 24]);
create_figure('Kragel Pain-Cog-Emo maps', 1, 3);
stats = image_similarity_plot(con_data_obj, 'average', 'mapset', bpls_wholebrain, 'networknames', names_wholebrain, 'nofigure');
Table of correlations Group:1
--------------------------------------
T-test on Fisher's r to Z transformed point-biserial correlations
R_avg T P sig
Pain Wholebrain -0.0034 -0.6172 0.5388 0.0000
Cog Wholebrain -0.0227 -4.6149 0.0000 1.0000
Emo Wholebrain 0.0236 3.5770 0.0006 1.0000
barplot_columns(stats.r', 'nofigure', 'colors', {[1 .9 0] [.2 .2 1] [1 .2 .2]}, 'names', names_wholebrain)
Col 1: Pain Wholebrain Col 2: Cog Wholebrain Col 3: Emo Wholebrain
---------------------------------------------
Tests of column means against zero
---------------------------------------------
Name Mean_Value Std_Error T P Cohens_d
___________________ __________ _________ ________ __________ _________
{'Pain Wholebrain'} -0.003412 0.0055341 -0.61654 0.53927 -0.068086
{'Cog Wholebrain' } -0.02274 0.0049256 -4.6167 1.4461e-05 -0.50983
{'Emo Wholebrain' } 0.023596 0.0065929 3.579 0.000586 0.39524
ans =
fig_han: [1×1 struct]
axis_han: [1×1 Axes]
bar_han1: [1×1 Bar]
bar_han: {[1×1 Bar] [1×1 Bar] [1×1 Bar]}
errorbar_han: {[1×1 ErrorBar] [1×1 ErrorBar] [1×1 ErrorBar]}
point_han1: {82×3 cell}
text_han: {82×3 cell}
point_han: {82×3 cell}
star_handles: [16.0009 17.0009 18.0009]
ylabel('Pattern similarity (r)');
title('Similarity (r) with patterns')
test_data_obj = resample_space(con_data_obj, bpls_wholebrain);
csim(:, i) = canlab_pattern_similarity(test_data_obj.dat, bpls_wholebrain.dat(:, i), 'cosine_similarity');
end
Warning: Some images have zero values in some of the 411578 voxels in weight mask. These will be excluded from similarity analysis image-wise.
Number of zero or NaN values within weight mask, by input image:
83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83
Warning: Some images have zero values in some of the 411578 voxels in weight mask. These will be excluded from similarity analysis image-wise.
Number of zero or NaN values within weight mask, by input image:
83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83
Warning: Some images have zero values in some of the 411578 voxels in weight mask. These will be excluded from similarity analysis image-wise.
Number of zero or NaN values within weight mask, by input image:
83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83
barplot_columns(csim, 'nofigure', 'colors', {[1 .9 0] [.2 .2 1] [1 .2 .2]}, 'names', names_wholebrain)
Col 1: Pain Wholebrain Col 2: Cog Wholebrain Col 3: Emo Wholebrain
---------------------------------------------
Tests of column means against zero
---------------------------------------------
Name Mean_Value Std_Error T P Cohens_d
___________________ __________ _________ ________ __________ _________
{'Pain Wholebrain'} -0.0033547 0.0054801 -0.61217 0.54214 -0.067602
{'Cog Wholebrain' } -0.021921 0.0047794 -4.5866 1.622e-05 -0.5065
{'Emo Wholebrain' } 0.02341 0.0066205 3.5359 0.00067522 0.39048
ans =
fig_han: [1×1 struct]
axis_han: [1×1 Axes]
bar_han1: [1×1 Bar]
bar_han: {[1×1 Bar] [1×1 Bar] [1×1 Bar]}
errorbar_han: {[1×1 ErrorBar] [1×1 ErrorBar] [1×1 ErrorBar]}
point_han1: {82×3 cell}
text_han: {82×3 cell}
point_han: {82×3 cell}
star_handles: [19.0009 20.0009 21.0009]
ylabel('Pattern similarity (cosine sim)');
title('Pattern response (cosine similarity)')
contrast_of_interest = 'motor'
contrast_of_interest = 'motor'
mount_dir = '/Volumes/spacetop_projects_cue/analysis/fmri/spm/univariate/model01_6cond_ttl1/1stlevel';
contrast_name = {'P_VC_cue_high_gt_low', 'V_PC_cue_high_gt_low', 'C_PV_cue_high_gt_low', ...
'P_VC_stimlin_high_gt_low', 'V_PC_stimlin_high_gt_low', 'C_PV_stimlin_high_gt_low',...
'P_VC_stimquad_med_gt_other', 'V_PC_stimquad_med_gt_other', 'C_PV_stimquad_med_gt_other',...
'P_VC_cue_int_stimlin','V_PC_cue_int_stimlin', 'C_PV_cue_int_stimlin',...
'P_VC_cue_int_stimquad','V_PC_cue_int_stimquad','C_PV_cue_int_stimquad',...
'P_simple_cue_high_gt_low', 'V_simple_cue_high_gt_low', 'C_simple_cue_high_gt_low', ...
'P_simple_stimlin_high_gt_low', 'V_simple_stimlin_high_gt_low', 'C_simple_stimlin_high_gt_low',...
'P_simple_stimquad_med_gt_other', 'V_simple_stimquad_med_gt_other', 'C_simple_stimquad_med_gt_other',...
'P_simple_cue_int_stimlin', 'V_simple_cue_int_stimlin', 'C_simple_cue_int_stimlin',...
'P_simple_cue_int_stimquad','V_simple_cue_int_stimquad','C_simple_cue_int_stimquad'
index = find(strcmp(contrast_name, contrast_of_interest));
con_name = sprintf('*con_%04d.nii', index);
con_list = dir(fullfile(mount_dir, '*', con_name));
con_fldr = {con_list.folder}; fname = {con_list.name};
con_files = strcat(con_fldr,'/', fname)';
con_data_obj = fmri_data(con_files);
Using default mask: /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/canlab_canonical_brains/Canonical_brains_surfaces/brainmask_canlab.nii
Direct calls to spm_defauts are deprecated.
Please use spm('Defaults',modality) or spm_get_defaults instead.
loading mask. mapping volumes.
checking that dimensions and voxel sizes of volumes are the same.
Pre-allocating data array. Needed: 33545232 bytes
Loading image number: 84
Elapsed time is 3.299919 seconds.
Image names entered, but fullpath attribute is empty. Getting path info.
Number of unique values in dataset: 8007789 Bit rate: 22.93 bits
disp(strcat("current length is ", num2str(size(con_data_obj.dat,2))));
%for s = 1:length(wh_outlier_corr)
% disp(strcat("-------subject", num2str(s), "------"))
con.dat = con_data_obj.dat(:,~wh_outlier_corr);
con.image_names = con_data_obj.image_names(~wh_outlier_corr,:);
con.fullpath = con_data_obj.fullpath(~wh_outlier_corr,:);
con.files_exist = con_data_obj.files_exist(~wh_outlier_corr,:);
disp(strcat("after removing ", num2str(sum(wh_outlier_corr)), " participants, size is now ",num2str(size(con.dat,2))));
after removing 1 participants, size is now 83
[path,n,e] = fileparts(con_fldr(wh_outlier_corr));
disp(strcat("participants that are outliers:... ", n));
participants that are outliers:... sub-0080
[obj, names] = load_image_set('pain_cog_emo');
Loaded images:
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_pMCC_Pain.nii
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_aMCC_Pain.nii
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_pACC_Pain.nii
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_sgACC_Pain.nii
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_vmPFC_Pain.nii
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_dMFC_Pain.nii
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_MFC_Pain.nii
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_Wholebrain_Pain.nii
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_pMCC_Cognitive_Control.nii
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_aMCC_Cognitive_Control.nii
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_pACC_Cognitive_Control.nii
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_sgACC_Cognitive_Control.nii
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_vmPFC_Cognitive_Control.nii
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_dMFC_Cognitive_Control.nii
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_MFC_Cognitive_Control.nii
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_Wholebrain_Cognitive_Control.nii
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_pMCC_Negative_Emotion.nii
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_aMCC_Negative_Emotion.nii
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_pACC_Negative_Emotion.nii
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_sgACC_Negative_Emotion.nii
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_vmPFC_Negative_Emotion.nii
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_dMFC_Negative_Emotion.nii
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_MFC_Negative_Emotion.nii
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_Wholebrain_Negative_Emotion.nii
bpls_wholebrain = get_wh_image(obj, [8 16 24]);
names_wholebrain = names([8 16 24]);
create_figure('Kragel Pain-Cog-Emo maps', 1, 3);
stats = image_similarity_plot(con_data_obj, 'average', 'mapset', bpls_wholebrain, 'networknames', names_wholebrain, 'nofigure');
Table of correlations Group:1
--------------------------------------
T-test on Fisher's r to Z transformed point-biserial correlations
R_avg T P sig
Pain Wholebrain -0.0181 -4.5639 0.0000 1.0000
Cog Wholebrain 0.0305 13.0772 0.0000 1.0000
Emo Wholebrain -0.0102 -2.6104 0.0107 1.0000
barplot_columns(stats.r', 'nofigure', 'colors', {[1 .9 0] [.2 .2 1] [1 .2 .2]}, 'names', names_wholebrain)
Col 1: Pain Wholebrain Col 2: Cog Wholebrain Col 3: Emo Wholebrain
---------------------------------------------
Tests of column means against zero
---------------------------------------------
Name Mean_Value Std_Error T P Cohens_d
___________________ __________ _________ _______ __________ ________
{'Pain Wholebrain'} -0.018062 0.0039573 -4.5643 1.7191e-05 -0.49801
{'Cog Wholebrain' } 0.030509 0.0023311 13.088 2.2204e-15 1.428
{'Emo Wholebrain' } -0.010229 0.0039174 -2.6113 0.010703 -0.28491
ans =
fig_han: [1×1 struct]
axis_han: [1×1 Axes]
bar_han1: [1×1 Bar]
bar_han: {[1×1 Bar] [1×1 Bar] [1×1 Bar]}
errorbar_han: {[1×1 ErrorBar] [1×1 ErrorBar] [1×1 ErrorBar]}
point_han1: {84×3 cell}
text_han: {84×3 cell}
point_han: {84×3 cell}
star_handles: [16.0010 17.0010 18.0010]
ylabel('Pattern similarity (r)');
title('Similarity (r) with patterns')
test_data_obj = resample_space(con_data_obj, bpls_wholebrain);
csim(:, i) = canlab_pattern_similarity(test_data_obj.dat, bpls_wholebrain.dat(:, i), 'cosine_similarity');
end
Warning: Some images have zero values in some of the 411578 voxels in weight mask. These will be excluded from similarity analysis image-wise.
Number of zero or NaN values within weight mask, by input image:
83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83
Warning: Some images have zero values in some of the 411578 voxels in weight mask. These will be excluded from similarity analysis image-wise.
Number of zero or NaN values within weight mask, by input image:
83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83
Warning: Some images have zero values in some of the 411578 voxels in weight mask. These will be excluded from similarity analysis image-wise.
Number of zero or NaN values within weight mask, by input image:
83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83
barplot_columns(csim, 'nofigure', 'colors', {[1 .9 0] [.2 .2 1] [1 .2 .2]}, 'names', names_wholebrain)
Col 1: Pain Wholebrain Col 2: Cog Wholebrain Col 3: Emo Wholebrain
---------------------------------------------
Tests of column means against zero
---------------------------------------------
Name Mean_Value Std_Error T P Cohens_d
___________________ __________ _________ _______ __________ ________
{'Pain Wholebrain'} -0.013415 0.0035724 -3.7553 0.00032038 -0.40973
{'Cog Wholebrain' } 0.027693 0.0021374 12.956 2.2204e-15 1.4137
{'Emo Wholebrain' } -0.012661 0.0035738 -3.5427 0.00065307 -0.38654
ans =
fig_han: [1×1 struct]
axis_han: [1×1 Axes]
bar_han1: [1×1 Bar]
bar_han: {[1×1 Bar] [1×1 Bar] [1×1 Bar]}
errorbar_han: {[1×1 ErrorBar] [1×1 ErrorBar] [1×1 ErrorBar]}
point_han1: {84×3 cell}
text_han: {84×3 cell}
point_han: {84×3 cell}
star_handles: [19.0010 20.0010 21.0010]
ylabel('Pattern similarity (cosine sim)');
title('Pattern response (cosine similarity)')
% pubfilename = '6cond_cueeffect_contrast.mlx';
% p = struct('useNewFigure', false, 'maxHeight', 800, 'maxWidth', 800, ...
% 'format', 'html', 'outputDir', pubdir, ...
% 'showCode', true, 'stylesheet', which('mxdom2simplehtml_CANlab.xsl'));
% htmlfile = publish(pubfilename, p);